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Study of Combined Heat and Power Integrated Carbon Capture and Compression at an Existing Cement Manufacturing Facility

2023· article· W7117239965 on OpenAlexaffabout
Wayuta Srisang, Brent Jacobs, Yuewu Feng, Puttipong Tantikhajorngosol, Doug Daverne, Conway Nelson

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsInternational Development Research Centre
Fundersnot available
KeywordsCementReliability (semiconductor)Carbon capture and storage (timeline)Power stationProduction (economics)Process (computing)Greenhouse gasElectricity generationCombustion

Abstract

fetched live from OpenAlex

As countries and organizations establish net zero targets and pathways to achieve these targets, the role of carbon capture, utilization and storage (CCS/CCUS) is widely recognized. The role of CCS is particularly important in the cement industry where over half of the CO2emissions are inherent to the cement manufacturing process and, therefore, need to be mitigated by CCS or other means.A feasibility study was completed for an existing cement manufacturing facility in Western Canada; the so-called CCS Feasibility Study. The study uncovered a number of innovations that can be applied to reduce the overall risk profile of the project from an economic and reliability perspective. This paper will share the results of evaluating options for a combined heat and power (CHP) plant to supply energy to the CCS facility. Several CHP options were evaluated, and a combustion turbine selection was recommended. The addition of CHP can increase the project NPV by running consistently and providing power to the grid when the cement plant isn’t running.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.252
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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